code: def lowerBidiagonalMatrix(m, n):
      # This is a simple example for testing LSMR.
      # It uses the leading m*n submatrix from
      # A = [ 1
      #       1 2
      #         2 3
      #           3 4
      #             ...
      #               n ]
      # suitably padded by zeros.
      #
      # 04 Jun 2010: First version for distribution with lsmr.py
      if m <= n:
          row = hstack((arange(m, dtype=int), arange(1, m, dtype=int)))
          col = hstack((arange(m, dtype=int), arange(m - 1, dtype=int)))
          data = hstack(
              (arange(1, m + 1, dtype=float), arange(1, m, dtype=float))
          )
          return coo_matrix((data, (row, col)), shape=(m, n))
      else:
          row = hstack(
              (arange(n, dtype=int), arange(1, n + 1, dtype=int))
          )
          col = hstack((arange(n, dtype=int), arange(n, dtype=int)))
          data = hstack(
              (
                  arange(1, n + 1, dtype=float),
                  arange(1, n + 1, dtype=float),
              )
          )
          return coo_matrix((data, (row, col)), shape=(m, n))
  
imports: from numpy import array, arange, eye, zeros, ones, transpose, hstack
  from numpy.linalg import norm
  from numpy.testing import assert_allclose
  import pytest
  from scipy.sparse import coo_matrix
  from scipy.sparse.linalg._interface import aslinearoperator
  from scipy.sparse.linalg import lsmr
  from .test_lsqr import G, b
label_desc: Use image node_id 1 for calling a global function with example usage: lowerBidiagonalMatrix(m, n) and returns: coo_matrix, coo_matrix